Method and apparatus for automatic correlation of multi-channel interactions

ABSTRACT

A method and apparatus for multi-channel categorization, comprising capturing a vocal interaction and a non-vocal interaction, using logging or capturing devices; retrieving a first word from the vocal interaction and a second word from the non-vocal interaction; assigning the vocal interaction into a first category using the first word; assigning the non-vocal interaction into a second category using the second word; and associating the first category and the second category into a multi-channel category, thus aggregating the vocal interaction and the non-vocal interaction.

TECHNICAL FIELD

The present disclosure relates to interaction analysis in general, andto a method and apparatus for correlating interactions captured throughdifferent channels, in particular.

BACKGROUND

Large organizations, such as commercial organizations, financialorganizations or public safety organizations conduct numerousinteractions with customers, users, suppliers or other persons on adaily basis. A large part of these interactions are vocal, or at leastcomprise a vocal component, while others may include text in formatssuch as e-mails, chats, accessing through the web or others.

However, interactions received on different channels are usually handledby different staff and in different methods. For example, vocalinteractions as described above are handled by call center personnel,which sometimes belong to an external or even offshore party and are notpart of the organization, while other interactions are handled by otherteams.

Thus, this division may be limiting when trying to gain business insightas related to the organization as a whole, for example revealing andunderstanding customer satisfaction or business and operational issuesof the organization. The limitation is especially acute when a customercontacts the organization several times by different channels, or whendifferent customers contact the call center using different channels,concerning the same or similar issues.

There is therefore a need in the art for a method and apparatus thatwill enable an organization to identify business issues and relate tocustomer interactions regardless of the channels through which they arereceived.

SUMMARY

A method and apparatus for categorizing interactions in a call center ofan organization.

One aspect of the disclosure relates to a method for categorizinginteractions in a call center of an organization, the method comprising:capturing one or more vocal interactions and one or more non-vocalinteraction, using logging or capturing devices; retrieving one or morefirst words from one or more of the vocal interactions; retrieving oneor more second words from one or more of the non-vocal interactions;assigning one or more of the vocal interactions into a first categoryusing any of the first words; assigning one or more of the non-vocalinteractions into a second category using any of the second words; andassociating the first category and the second category into amulti-channel category, thus aggregating the one or more vocalinteractions and the one or more non-vocal interactions. Within themethod, the first category and the second category are optionallypre-determined sub-categories, and associating the first category andthe second category optionally comprises unifying the pre-determinedsub-categories. Within the method, the first category and the secondcategory are optionally clusters, and associating the first category andthe second category optionally comprises unifying the clusters. Themethod can further comprise performing initial filtering for selectinginteractions to be processed. Within the method, the initial filteringoptionally relates to Computer-Telephony-Integration data or toCustomer-Relationship-Management data or to meta data associated withany of the vocal interactions or the non-vocal interactions. The methodcan further comprise filtering the vocal interactions. Within themethod, filtering the vocal interactions optionally relates to emotiondetection, talk analysis, accent identification or languageidentification. The method can further comprise filtering non-vocalinteractions. Within the method, filtering the non-vocal interactionoptionally relates to sentiment analysis. The method can furthercomprise normalization and injection of any of the vocal interactions orthe non-vocal interactions into a unified format. The method can furthercomprise analyzing the multi-channel category.

Another aspect of the disclosure relates to an apparatus formulti-channel categorizing interactions in a call center of anorganization, comprising: a logging or capturing component for capturingone or more vocal interactions and one or more non-vocal interactions;an audio analysis engine for retrieving one or more first words from anyof the vocal interactions; a text analysis engine for retrieving one ormore second words from any of the non-vocal interactions; a groupingcomponent for assigning the any of the vocal interactions into a firstcategory using any of the first word and assigning any of the non-vocalinteractions into a second category using any of the second words; andan aggregation component for associating the first category and thesecond category into a multi-channel category, thus aggregating any ofthe vocal interactions and any of the non-vocal interaction. Within theapparatus, the first category and the second category are optionallypre-determined sub-categories, and the aggregation component optionallyunifies the first category and the second category into one or morecategories. Within the apparatus, the first category and the secondcategory are optionally clusters, and the aggregation componentoptionally unifies the first category and the second category into oneor more clusters. Within the apparatus, the audio analysis engine isoptionally a word spotting engine or a speech to text engine. Theapparatus can further comprise one or more of the engines selected fromthe group consisting of: emotion detection engine; talk analysis engine;call flow analysis engine; accent identification engine; and languageidentification engine. The apparatus can further comprise anormalization and injection component for normalizing and injecting anyof the vocal interactions or the non-vocal interactions into a unifiedformat. The apparatus can further comprise an advanced analysis enginefor analyzing the multi-channel category. The apparatus can furthercomprise a filtering component for filtering out interactions unsuitablefor further analysis.

Yet another aspect of the disclosure relates to a computer readablestorage medium containing a set of instructions for a general purposecomputer, the set of instructions comprising capturing one or more vocalinteractions and one or more non-vocal interactions, using logging orcapturing devices; retrieving one or more first words from any of thevocal interactions; retrieving one or more second words from any of thenon-vocal interaction; assigning any of the vocal interactions into afirst category using any of the first words; assigning any of thenon-vocal interactions into a second category using any of the secondwords; and associating the first category and the second category into amulti-channel category, thus aggregating any of the vocal interactionsand the non-vocal interactions.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will be understood and appreciated more fully fromthe following detailed description taken in conjunction with thedrawings in which corresponding or like numerals or characters indicatecorresponding or like components. Unless indicated otherwise, thedrawings provide exemplary embodiments or aspects of the disclosure anddo not limit the scope of the disclosure. In the drawings:

FIG. 1 is a schematic illustration of an apparatus for real-timeanalytics and a typical environment in which the apparatus is used, inaccordance with the disclosure;

FIG. 2 is a schematic flowchart detailing the method of interactionanalytics, in accordance with the disclosure;

FIG. 3 is a detailed flowchart of method for interaction normalizationand injection, in accordance with the disclosure;

FIG. 4 is a detailed flowchart of method for multi-channelcategorization, in accordance with the disclosure; and

FIG. 5 is a schematic illustration of an exemplary embodiment of anapparatus for multi-channel categorization, in accordance with thedisclosure.

DETAILED DESCRIPTION

The disclosed subject matter is described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thesubject matter. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide processes for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

One technical problem dealt with by the disclosed subject matter relatesto information relevant to a customer of an organization which isdispersed over a number of channels such as phone, e-mail, chat orothers, optionally handled by multiple people wherein information is notpassed between the different entities, so that the client is handled ina suboptimal manner. A similar technical problem may occur with amultiplicity of customers dealing with similar problems through one ormore channels. Yet another technical problem is the non-uniform handlingmethods, skills and knowledge throughout the organization, so forexample one problem may be handled better by the call center, whileanother problem is handled better by chat personnel.

Technical aspects of the solution can relate to an apparatus and methodfor capturing interactions from various sources and channels, processingthem in analogous manners and unifying them into multi-channelcategories, so as to get information which is organization-wiserelevant, whether related to one customer or to a multiplicity ofcustomers.

The method and apparatus provide interaction analytics and multi-channelcategorization based on data extracted from the interactions, as well asmeta data related to the interactions. Calls are grouped automaticallybased on common data and content, thus gaining significant insightrelated to customer experience and other business issues. The insightsmay be relevant both for a specific customer whose multi-channelinteractions are being analyzed, and for the overall aggregate impact oncustomer service levels and operational efficiency in the organization.

For example, if a customer has contacted the call center regarding adispute three or more times via two or three different channels over thepast week, the system can generate an alert to a supervisor or anotherperson in charge.

On the organizational level, if several customers contacted the callcenter thorough different channels regarding the same topic, the systemwill group the different interactions from all channels into onecategory, and will generate reports or raise an alert addressing thebusiness issue.

In another aspect, linking interactions through a particular categorymay improve agent behavior, professionalism and performance, and callcenter core processes. For example, the method and apparatus may provideindication that a so customer has contacted the call center severaltimes and talked with different agents, so that the behavior andprofessionalism of different agents can be assessed and compared.

In another example, it may be measured that a first group of agentshandling chat interactions are able to solve a technical issue on thefirst call with average handling time of 5 minutes, while an agent groupwhich handles voice interactions solves the same technical issue aftertwo calls and total time of 10 minutes. Each agent group may be behavingin accordance with the training it received, but looking at the overallpicture may reveal that the guidelines and processes need to beadjusted, for example by improving technical knowledge transfer betweencategories.

The method and apparatus comprise normalizing the interactions andstoring them in a consistent manner regardless of the channels throughwhich they were captured, followed by categorizing the interactions intocategories, and then merging or uniting similar categories comprisinginteractions received through different channels. Then any type ofanalysis can be performed, including multi-channel analysis to revealdata from the interactions and categories.

Referring now to FIG. 1, showing a block diagram of the main componentsin the apparatus and in a typical environment in which the disclosedmethod and apparatus are used. The environment is preferably aninteraction-rich organization, typically a call center, a bank, atrading floor, an insurance company or another financial institute, apublic safety contact center, an interception center of a lawenforcement organization, a service provider, an internet contentdelivery company with multimedia search needs or content deliveryprograms, or the like. Segments, including broadcasts, interactions withcustomers, users, organization members, suppliers or other parties arecaptured, thus generating input information of various types. Theinformation types optionally include auditory segments, video segments,textual interactions, and additional data. The capturing of voiceinteractions, or the vocal part of other interactions, such as video,can employ many forms, formats, and technologies, including trunk side,extension side, summed audio, separate audio, various encoding anddecoding protocols such as G729, G726, G723.1, and the like.

The interactions are captured using capturing or logging components 100.Vocal interactions usually include telephone or voice over IPinteractions 112, including interactions through telephone of any kind,including landline, mobile, satellite phone or others. The voicetypically passes through a PABX (not shown), which in addition to thevoices of two or more sides participating in the interaction collectsadditional information discussed below. Further vocal interactions canbe captured through voice over IP channels, which possibly pass througha voice over IP server (not shown). It will be appreciated that voicemessages are optionally captured and processed as well, and that thehandling is not limited to two-sided conversations. The interactions canfurther include face-to-face interactions, such as those recorded in awalk-in-center, microphone, intercom, vocal input by external systems,broadcasts, files, streams, video conferences which comprise an audiocomponent, and additional sources of auditory data.

Other captured interactions may include e-mails 116, chat sessions 120,survey results 124 or social media content 128. These interactions canbe captured using any existing technologies, such as various encodingand protocols as detailed above for voice segments. Screen capturedevices can be used for capturing events occurring on an agents' screen,such as entered text, typing into fields, activating controls, or anyother data which may be structured and stored as a collection of screenevents rather than screen capture.

The interaction capture may be performed by data collection enabled bydifferent connectors for each channel type and vendor. For example, acorresponding connector may be implemented for each type of mail server,in order to capture incoming and outgoing mail messages exchangedthrough the server. Other connectors may be implemented from chatservers, survey systems and social media servers.

Each connector may connect to the relevant text interaction service orsource, collect relevant data which may include the actual contentexchanged as well as meta-data, optionally based on pre-defined rulesregulating which interactions are to be captured.

It will be appreciated that the captured sources are not limited to theones discussed above, but that other sources can be used as well,including independent data sources or auxiliary data sources such asComputer-Telephony-Integration (CTI) systems, information fromCustomer-Relationship-Management (CRM) systems, or the like.

Data from all the above-mentioned sources and others is captured and maybe logged by capturing/logging component 132. Capturing/loggingcomponent 132 comprises a computing platform executing one or morecomputer applications as detailed below. The captured data may be storedin storage 134 which is preferably a mass storage device, for example anoptical storage device such as a CD, a DVD, or a laser disk; a magneticstorage device such as a tape, a hard disk, Storage Area Network (SAN),a Network Attached Storage (NAS), or others; a semiconductor storagedevice such as Flash device, memory stick, or the like. The storage canbe common or separate for different types of captured segments anddifferent types of additional data. The storage can be located onsitewhere the segments or some of them are captured, or in a remotelocation. The capturing or the storage components can serve one or moresites of a multi-site organization. A part of, or storage additional tostorage 134 may store data related to the categorization such ascategories or sub-categories, criteria, associated actions, or the like.Storage 134 may also contain data and programs relevant for audioanalysis, such as speech models, language models, lists of words to bespotted, or the like.

It will be appreciated that interactions may be captured in accordancewith rules. In one example, only interactions with external entities maybe recorded, in other examples only interactions with VIP customers arerecorded, or the like.

Interaction analytics component 136 receives the interactions andanalyzes them in a multi-channel manner, so that information receivedfrom different sources is normalized and processed, as detailed inassociation with FIGS. 2-5 below.

In some embodiments, the interactions may be streamed into interactionanalytics component 136 and analyzed as they are being received. Inother embodiments, the interactions may be received as one or morechunks, for example 2-30 seconds chunk of audio, text chunks of chatsessions, or the like.

The results of interaction analytics component 136 can be used in avariety of applications, such as but not limited to any of thefollowing: first contact resolution component 140, which providesassistance to a handling agent so that the customer issue is solved onthe first interaction; churn reduction component 142 for churn-relatedanalysis aimed at reducing the churn rate of customers; customerexperience analysis component 144 for analyzing different aspects ofcustomer experience across the organization; sales effectivenesscomponent 148 for enhancing the effectiveness of sales forces, re-salesor the like; handling time optimization component 152 for optimizing thehandling time by agents, for example by enhancing the agent training,better assignment of agents, knowledge passing within the organization,or the like; collection optimization component 156 for enhancing therules in accordance with which interactions are captured, or the like.

It will be appreciated that any different, fewer or additional actionscan be used for various organizations and environments. Some componentscan be unified, while the activity of other described components can besplit among multiple components. It will also be appreciated that someimplementation components, such as process flow components, storagemanagement components, user and security administration components,audio enhancement components, audio quality assurance components orothers can be used.

The apparatus may comprise one or more computing platforms, executingcomponents for carrying out the disclosed steps. Each computing platformcan be a general purpose computer such as a personal computer, amainframe computer, or any other type of computing platform that isprovisioned with a memory device (not shown), a CPU or microprocessordevice, and several I/O ports (not shown). The components are preferablycomponents comprising one or more collections of computer instructions,such as libraries, executables, modules, or the like, programmed in anyprogramming language such as C, C++, C#, Java or others, and developedunder any development environment, such as .Net, J2EE or others.Alternatively, the apparatus and methods can be implemented as firmwareported for a specific processor such as digital signal processor (DSP)or microcontrollers, or can be implemented as hardware or configurablehardware such as field programmable gate array (FPGA) or applicationspecific integrated circuit (ASIC). The software components can beexecuted on one platform or on multiple platforms wherein data can betransferred from one computing platform to another via a communicationchannel, such as the Internet, Intranet, Local area network (LAN), widearea network (WAN), or via a device such as CDROM, disk on key, portabledisk or others.

Referring now to FIG. 2, showing a schematic flowchart detailing themethod of interaction analytics component 136 of FIG. 1. The method,generally indicated 136′, comprises normalization and injection 204,categorization 208 and advanced analysis 212.

At normalization and injection 204 the interactions and metadata arenormalized so that they can be processed in analogous manner, andinjected into unified storage. Normalization and injection 204 isfurther detailed in association with FIG. 3 below.

On categorization 208 the data is categorized into multi-channelcategories, such that each category may contain interactions capturedfrom two or more different channels.

On advanced analysis 212, the data and the categories into which eachinteraction is categorized are further analyzed to reveal multi-channelor organization-wide information.

Referring now to FIG. 3, showing a detailed flowchart of normalizationand injection 204.

Normalization and injection 204 receives interactions of differentsources, which may include but are not limited to captured voiceinteractions 300 which may include telephone interactions, voice over IPinteractions or others; captured e-mail interactions 304; captured chatsessions 308; captured surveys 312 which may be in any form such astable, text, proprietary format, or the like; or captured social media316 which may include data items such as text from social networks.

The textual data, including captured e-mail interactions 304, capturedchat sessions 308, captured surveys 312 and captured social media 316are then parsed and formatted at text parsing and formatting 320 into aunified format. Parsing the text provides for extracting data related toan interaction, and for converting its content into a unified format sothat interactions captured from different channel types are laterhandled in the same manner. The unified format may comprise for example,the text, the text as stemmed with the stop words removed, in a tabularform in which the number of occurrences for each word is indicated, orthe like.

The captured interactions are then stored in interaction database 324.Additional data such as the interactions in unified format or meta datasuch as date and time of an interaction, identifying data of theinteraction or the like, are stored in content storage 324.

Once the text interactions and the additional data are stored in aunified format, text analysis 332 performs normalization of the unifiedformat and different types of text analysis, which may include but isnot limited to Natural Language Processing (NLP), key phrases detectionor scoring, sentiment analysis, or the like.

The analysis results may be stored on 336 in any format uniform for allchannel types, which may be related to as a concordance or index file,for example in XML or JSON format. The analysis results may also beindexed into an indexed database 340 which enables text searches andadvanced aggregated analysis based on text and meta-data, such as aLucene Text Indexer database.

The vocal interactions are also stored on interaction database 324 whiletheir meta data, including for example date and time of an interaction,called number, calling number, CTI information, CRM information or thelike are stored on content storage 328.

Physically, interaction database 324 and content storage 328 can resideon the same storage device or each of them can reside on one or moredifferent devices.

Referring now to FIG. 4, showing a detailed flowchart of an exemplaryembodiment of multi-channel categorization step 208 of FIG. 2.

The input to the categorization method is received from interactiondatabase 324 and content storage 328 as populated on normalization andinjection step 204.

The categorization process is operative in that interactions areeventually categorized into multi-channel main categories which includerelated interactions captured from different channel types.

On 402, initial categories are defined, which are mainly based ontechnical data, such as meta data of the interactions, CTI information,CRM information, spoken language, or the like. The initial categoriesmay be defined by rules, such as “VIP calls”, calls dialed to a specificnumber, or the like.

Voice interactions are filtered on voice categorization 404 into one ormore of the initial categories, based for example on the meta data ofthe interaction as stored in content storage 328.

A textual interaction is categorized on text categorization 408 into oneor more of the initial categories, based on the text, on the informationas indexed or on the meta data of the interaction as stored in contentstorage 328.

On optional initial filtering 412, the interactions that werecategorized into one or more of the initial categories are filtered andone or more of them are passed to further processing.

The filtered interactions, or all interactions if filtering is omitted,are processed in accordance with their type, separately for voiceinteractions and for text interactions.

Vocal interactions may be further filtered on audio filtering step 416using emotion analysis, talk analysis, accent analysis or any otheranalysis which does not require significant processing resources, andmay not provide lexical content of the interaction.

The audio interactions that passed filtering 416 are passed to optionalstep 424 in which particular words or phrases may be searched for by aphonetic engine, followed by transcribing the calls 428 and indexing theresults in indexed database 340. In some exemplary embodiments, only thecalls that matched the word or phrase query are been transcribed. Thus,the content of the interaction is extracted and indexed. The phoneticengine search searches for a particular list of words rather than a fulltranscription, but provides these words with higher accuracy. Thereforephonetic search may be employed for the most important words, and thelocated instances of these words are combined into the fulltranscription generated on step 428.

The textual interactions may also undergo optional additional filtering420, such as sentiment analysis in which words indicating emotions aresearched for. The interactions that passed the filtering, i.e., containsentiment, may go through word search 432.

Using the results of word search 424 and transcription 428 for vocalinteractions, and word search 432 for non-vocal interactions such astextual interactions, the calls are assigned into categories onassignment 436, for example by clustering or by sub-categorization intosub-categories.

It will be appreciated that the word search can be any combination ofword search 424 and word search 432 with and/or and possibly otheroperators. For example a search can be conducted for word 1 and any ofword 2 or word 3, i.e., word 1 AND (word 2 OR word 3).

Assignment 436 may use sub-categories defined on sub-category definitionstep 434. The sub-categories may be separate for vocal and textualinteractions, since for example different words may be used in spokenand written language. For example, in a vocal interaction in which acustomer is leaving a company he may include the word “leave”, while inwriting the customer may use “disconnect”.

Alternatively, assignment 436 can be implemented as a clustering step.In categorization, the categories are created in advance, and assignmentcriteria is defined for each category. The categories are thuspre-created and their definitions do not depend on the availableinteractions to be categorized. Also, in categorization, eachinteraction can be categorized into zero, one, or more categories.

In clustering, however, the interactions are divided into clusters suchthat: the clusters are not pre-defined but are rather created based onthe available interactions; and each interaction is assigned to exactlyone cluster.

On multi-channel categorization 440, related clusters or sub-categoriesare unified and correlated. Thus, if assignment 436 comprisescategorization, sub-categories related to the same subject but to two ormore different interaction types are unified, while if assignment 436comprises clustering, clusters related to the same subject but to two ormore different interaction types are unified,

For example, a “technical problem X” category may be created whichaggregates the vocal interactions categorized into “problem X telephonecalls”, and textual interactions categorized into “problem X e-mails”,“problem X chat sessions”, etc.

Correlating categories can be performed by a user using a user-interfacein which categories from different channels are explicitly combined. Inalternative embodiments, the correlation can be done automatically, byusing semantic inference engines to combine categories having the samesemantic context or information.

On storing step 444 the assigned interactions and their clusters orparent and sub-categorizations are stored in a categorized database 448,such as a multi-dimensional database for example online analyticalprocessing (OLAP) database.

Referring now to FIG. 5, showing an exemplary embodiment of an apparatusfor multi-channel categorization, which details component 136 of FIG. 1,and provides an embodiment for the method of FIG. 4.

The exemplary apparatus comprises communication component 500 whichenables communication among other components of the apparatus, andbetween the apparatus and components of the environment, such as storage134, logging and capturing component 132, or others. Communicationcomponent 500 can be a part of, or interface with any communicationsystem used within the organization or the environment shown in FIG. 1.

The apparatus further comprises database connectivity component 502 forstoring and retrieving information from one or more databases, includingraw interaction data and meta data, data extracted from theinteractions, or grouping information.

The apparatus comprises also activity flow manager 504 which manages thedata flow and control flow between the components within the apparatusor between the apparatus and the environment.

The apparatus comprises analysis engines 508, filtering and groupingcomponents 536, and auxiliary components 556.

Filtering and grouping components 536 comprise filtering component 538for filtering the interactions in order to select the interactions thatshould be further analyzed and categorized. For example, too short ortoo long interactions may not be suitable for such analysis and may befiltered out.

Filtering and grouping components 536 optionally comprise sub-categorydefinition component 540 for defining sub-categories into which theinteractions are assigned, based on the extracted information, andcategorization component 544 for categorizing an interaction into one ormore of the defined sub-categories. Alternatively, or in addition,filtering and grouping components 536 comprise clustering component 548which receives a collection of transcriptions or other textual itemsextracted from interactions, and divides the interaction into clusters.

Filtering and grouping components 536 further comprise aggregationcomponent 552 for aggregating two sub-categories or two clusters into aunified multi-channel category or cluster.

Analysis engines 508 are used for analyzing audio or text interactions.Analysis engines 508 may comprise any one or more of the enginesdetailed hereinafter.

Speech to text engine 512 may be any proprietary or third party enginefor transcribing an audio signal into text or a textual representation,and can be used for transcribing calls on step 428.

Word spotting engine 516 detects the appearance within the audio ofwords from a particular list. In some embodiments, after an initialindexing stage, any word can be search for, including words that wereunknown at indexing time, such as names of new products, competitors, orothers. Word spotting engine 516 and can be used for word searching onstep 424.

Call flow analysis engine 520 analyzes the flow of the interaction, suchas the number and timing of holds, number of transfers, or the like.Call flow analysis engine 520 can be used for initial filtering 412 oraudio filtering 416.

Talk analysis engine 524 analyzes the talking within an interaction: forwhat part of the interaction does each of the sides speak, silenceperiods on either side, mutual silence periods, talkover periods, or thelike. Talk analysis engine 524 can be used for initial filtering 412 oraudio filtering 416.

Emotion analysis engine 528 analyzes the emotional levels within theinteraction: when and at what intensity is emotion detected on eitherside of an interaction. Emotion analysis engine 528 can be used oninitial filtering 412 or audio filtering 416.

Text analysis engine 532 may comprise tools for analyzing text extractedfrom an interaction, for example for sentiment analysis 420, word search432 or others.

It will be appreciated that the components of analysis engines 508 maybe related to each other, such that results by one engine may affect theway another engine is used. For example, anger words can be spotted inareas in which high emotional levels are detected.

It will also be appreciated that analysis engines 508 may furthercomprise any other engines, including a preprocessing engine forenhancing the audio data, removing silence periods or noisy periods,rejecting audio segments of low quality, post processing engine, orothers.

Auxiliary components 556 may comprise normalization and injectioncomponent 560 for normalizing the interactions and injecting them into acommon database so they are handled regardless of their type.

Auxiliary components 556 also comprise advanced analysis engines forfurther analysis of the main categories or clusters, for retrievingadditional information form the interactions. The analysis may includeroot cause analysis, topic extraction, keyword extraction, linkanalysis, semantic inference, text entailment, text exploration,information retrieval or the like.

Auxiliary components 556 further comprise user interface components 568for presenting the information to a user including the sub-categories orclusters and the main multi-channel categories or clusters. Userinterface components 564 may further enable a user to aggregatesub-categories or clusters into main categories.

Assigning interactions of different types to the same “parent” categoryor cluster enables the correlation and uniform handling of interactionsregardless of their type or origin. This in turn enables better resourceallocation and knowledge passing within the organization.

It will be appreciated that the disclosed method and apparatus may havemultiple variations. For example, different criteria can be used todetermine whether an interaction is important or indicative enough andshould be categorized; whether and how it is determined whether aninteraction will be further processed, how to process the interactionsand how to combine different sub-categories or clusters, etc.

It will be appreciated by a person skilled in the art that the disclosedmethod and apparatus are exemplary only and that multiple otherimplementations and variations of the method and apparatus can bedesigned without deviating from the disclosure. In particular, differentdivision of functionality into components, and different order of stepsmay be exercised. It will be further appreciated that components of theapparatus or steps of the method can be implemented using proprietary orcommercial products.

While the disclosure has been described with reference to exemplaryembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the disclosure. Inaddition, many modifications may be made to adapt a particularsituation, material, step of component to the teachings withoutdeparting from the essential scope thereof. Therefore, it is intendedthat the disclosed subject matter not be limited to the particularembodiment disclosed as the best mode contemplated for carrying out thisinvention, but only by the claims that follow.

What is claimed is:
 1. A method for categorizing interactions in a callcenter of an organization, comprising: capturing in the call center atleast one vocal interaction and at least one non-vocal interaction,using logging or capturing devices, wherein the at least one vocalinteraction and the at least one non-vocal interaction are captured inaccordance with pre-defined rules that regulate which interaction is tobe captured, and wherein the at least one vocal interaction and the atleast one non-vocal interaction having dissimilar contents of commonsemantic context; retrieving at least one first word from the at leastone vocal interaction; retrieving at least one second word from the atleast one non-vocal interaction; assigning the at least one vocalinteraction into a first category, wherein the first category is basedon technical data of the at least one vocal interaction, using the atleast one first word; assigning the at least one non-vocal interactioninto a second category, wherein the second category is based ontechnical data of the at least one non-vocal interaction, using the atleast one second word; and associating the first category and the secondcategory into a multi-channel category based on the common semanticcontext thereof, thus aggregating the at least one vocal interaction andthe at least one non-vocal interaction.
 2. The method of claim 1 whereinthe first category and the second category are pre-determinedsub-categories, and wherein associating the first category and thesecond category comprises unifying the pre-determined sub-categories. 3.The method of claim 1 wherein the first category and the second categoryare clusters, and wherein associating the first category and the secondcategory comprises unifying the clusters.
 4. The method of claim 1further comprising performing initial filtering for selectinginteractions to be processed.
 5. The method of claim 4 wherein theinitial filtering relates to Computer-Telephony-Integration data or toCustomer-Relationship-Management data or to meta data associated withthe at least one vocal interaction or the at least one non-vocalinteraction.
 6. The method of claim 1 further comprising filtering theleast one vocal interaction.
 7. The method of claim 6 wherein filteringthe at least one vocal interaction relates to emotion detection, talkanalysis, accent identification or language identification.
 8. Themethod of claim 1 further comprising filtering the least one non-vocalinteraction.
 9. The method of claim 8 wherein filtering the at least onenon-vocal interaction relates to sentiment analysis.
 10. The method ofclaim 1 further comprising normalization and injection of the at leastone vocal interaction or the least one non-vocal interaction into aunified format.
 11. The method of claim 1 further comprising analyzingthe multi-channel category.
 12. An apparatus for multi-channelcategorizing interactions in a call center of an organization,comprising: a logging or capturing component for capturing in the callcenter at least one vocal interaction and at least one non-vocalinteraction, wherein the at least one vocal interaction and the at leastone non-vocal interaction are captured in accordance with pre-definedrules that regulate which interaction is to be captured, and wherein theat least one vocal interaction and the at least one non-vocalinteraction having dissimilar contents of common semantic context; anaudio analysis engine for retrieving at least one first word from the atleast one vocal interaction; a text analysis engine for retrieving atleast one second word from the at least one non-vocal interaction; agrouping component for assigning the at least one vocal interaction intoa first category using the at least one first word and assigning the atleast one non-vocal interaction into a second category using the atleast one second word, wherein the first category is based on technicaldata of the at least one vocal interaction and the second category isbased on the at least one non-vocal interaction; and an aggregationcomponent for associating the first category and the second categorybased on the common semantic context thereof into a multi-channelcategory, thus aggregating the at least one vocal interaction and the atleast one non-vocal interaction.
 13. The apparatus of claim 12 whereinthe first category and the second category are pre-determinedsub-categories, and wherein the aggregation component unifies the firstcategory and the second category into one or more categories.
 14. Theapparatus of claim 12 wherein the first category and the second categoryare clusters, and wherein the aggregation component unifies the firstcategory and the second category into one or more clusters.
 15. Theapparatus of claim 12 wherein the audio analysis engine is a wordspotting engine or a speech to text engine.
 16. The apparatus of claim12 further comprising one or more of the engines selected from the groupconsisting of: emotion detection engine; talk analysis engine; call flowanalysis engine; accent identification engine; and languageidentification engine.
 17. The apparatus of claim 12 further comprisinga normalization and injection component for normalizing and injectingthe at least one vocal interaction or the least one non-vocalinteraction into a unified format.
 18. The apparatus of claim 12 furthercomprising an advanced analysis engine for analyzing the multi-channelcategory.
 19. The apparatus of claim 12 further comprising a filteringcomponent for filtering out interactions unsuitable for furtheranalysis.
 20. A non-transitory computer readable storage mediumcontaining a set of instructions for a general purpose computer, the setof instructions comprising: capturing in the call center at least onevocal interaction and at least one non-vocal interaction, using loggingor capturing devices, wherein the at least one vocal interaction and theat least one non-vocal interaction are captured in accordance withpre-defined rules that regulate which interaction is to be captured, andwherein the at least one vocal interaction and the at least onenon-vocal interaction having dissimilar contents of common semanticcontext; retrieving at least one first word from the at least one vocalinteraction; retrieving at least one second word from the at least onenon-vocal interaction; assigning the at least one vocal interaction intoa first category, wherein the first category is based on technical dataof the at least one vocal interaction, using the at least one firstword; assigning the at least one non-vocal interaction into a secondcategory, wherein the second category is based on technical data of theat least one non-vocal interaction, using the at least one second word;and associating the first category and the second category into amulti-channel category based on the common semantic context thereof,thus aggregating the at least one vocal interaction and the at least onenon-vocal interaction.